We have had a couple of recent posts about firms trying to get more out of their call centers through psychometric data. The idea is that by classifying both customers and agents along psychometric dimensions, the firm can route callers of a particular characteristic to the type of agent that is most likely to lead to a good outcome (where “good” is presumably defined based on what the firm wants). I have to admit that I am not overly familiar with what psychometric measures they are using and am not sure how well they can measure these with infrequent customer contact. At some level, this starts to sound like whether a libra should date a taurus.
With that background, I found a recent Sloan Management Review article really fascinating (Matchmaking With Math: How Analytics Beats Intuition to Win Customers, Winter 2011). It is an interview with Cameron Hurst, a VP at Assurant Solutions. Assurant Solutions sells credit insurance. You pay them every month and then if you are, say, laid off they help cover your credit card bills. What customers pay ranges from $10 to $80 per month and it is not hard to see that some people may have second thoughts about paying that. What seemed like a good idea six months ago might not seem worth $20 now. Hence, their call center plays a key role in keeping customers. When customers get cold feet, it is up to call center agents to “re-sell” them on the product and retain the business. And that is where “affinity routing” comes in. They brought in some business analytics experts who already worked in the firm but doing actuarial work and such and asked them to look at the call center.
The first thing that was interesting about their approach was that rather than thinking about the average speed of answering phone calls, or the average “handle time,” or service level metrics, or individual customer experiences or using QA tools to find out what we did right and what we did wrong — all the things we usually consider when looking at customer and representative interaction — they started thinking of it purely from the perspective of, “We’ve got success and we’ve got failure.”
Success and failure are very easy things to establish in our business. You either retained a customer calling in to cancel or you didn’t. If you retained them, you did it by either a cross-sell, up-sell or down-sell.
So this is what they started asking: What was true when we retained a customer? What was true when we lost a customer? What was false when we retained a customer? And what was false when we lost a customer? For example, we learned that certain CSRs generally performed better with customers in higher premium categories while others did not. These are a few of the discoveries we made, but there were more. Putting these many independent variables together into scoring models gave us the basis for our affinity-based routing.
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